Joint Compressive Tracking for Multimodal Target

Multimodal tracking is a challenging task.Most of the existing methods only consider the fusion of different features from single image or the fusion of identical feature from different modal images.In order to integrate them naturally,a unified multimodal tracking framework based on joint compressive sensing is proposed in this paper.Our framework can integrate different features extracted from single image or different modal images,and provide the flexibility to arbitrarily add or remove feature.We formulate the multimodal tracking as a joint minimization problem of multiple ℓ₁-norms with inequality constraint of multiple ℓ₂-norms,and derive a customized augmented Lagrange multiplier algorithm to solve the minimization problem,so that efficient tracking with both low computational burden and high accuracy.Besides,a collaborative template update scheme induced by sparsity concentration index is developed to screen out the best templates throughout the tracking procedure.The experiments are carried out on DCU,OTCBVS,BEPMDS,OTB50 and VOT-TIR image datasets by frame tracking method,and the experimental results show the average tracking accuracy,success rate and speed of our method is 0.96,0.91 and 3.48 respectively.